Determinants influencing the profitability of listed food processing firms in Vietnam

heet. 
Total Debt to Total Equity and Long Term Debt (TDTELT) 
TDTELT has a positive influence on dependent variables of ROA. TDTELT is a measurement of a firm's financial leverage. It 
is calculated by taking the firm’s interest-bearing debt, both short and long-term liabilities and dividing it by the long-term debt 
plus shareholders’ equity. TDTELT gives analysts and investors a better idea of a firm's capital structure and whether or not the 
company is a suitable investment. All else being equal, the higher the TDTELT ratio, the riskier the firm to be invested in. This 
finding has the same results with the research of Nguyen (2016) in Vietnam. 
Fixed Assets to Total Assets (FATA) impacts on ROA but it has not been statistically significant on them. 
Adjusted Model 
To overcome the above defects of the model, we employ different regression methods, depending on the type of defects. As for 
models with dependent variable of Return on Assets (ROA) that heteroscedasticity exists, this error would be fixed by adding 
the "robust" command to the end of the FEM regression statement. The results are obtained as shown in Table 10, below: 
Table 10 
Estimated Results of Regression Model ROA 
 Equation 1a Equation 1b 
DR -0.266642 
 (-9.18) 
SDR -0.2575729 
 (-8.30) 
LDR -0.2916419 
 (-6.28) 
FATA -0.1233716 -0.1175987 
 (-1.94) (-1.92) 
TDTELT 0.0245583 0.0245643 
 (6.98) (6.37) 
SIZE 0.0055286 0.0050401 
 (0.41) (0.37) 
cons 0.21168 0.2103563 
 (2.06) (2.04) 
N 245 245 
t statistics in parenthese * p<0.05, ** p<0.01, *** p<0.001 
According to Table 10, variables of Debt ratio (DR), Short-term debt ratio (SDR), Long-term debt ratio (LDR) have the negative 
impact with Return on Assets (ROA). On the other hand, TDTELT has a positive impact on ROA. In addition, FATA and SIZE impact 
 448
on ROA but have not statistically significant on it. 
Based on the regression results presented in Table 10, we give regression equations of the following form: 
ROA = 0.21168 – 0.266642 × DR - 0.1233716 × FATA + 0.0245583 × TDTELT + 0.050401 × SIZE + e (1) 
ROA = 0.2103563 - 0.2575729 × SDR – 0.2916419× LDR - 0.1175987 × FATA + 0.0245643 × TDTELT + 0.050401 
× SIZE + e 
(2) 
Eq. (1) means: 
+ β1 = -0.266642 indicates that when the DR increases by 1 time, in the condition that other factors remained constant, the 
average ROA decreases by 0.266642 times. 
+ β2 = -0.1233716 reveals that when the FATA increases 1 time, in the condition that other factors remain unchanged, ROA 
will decrease by 0.1233716 times. 
+ β3 = 0.0245583 shows that when TDTELT increases by 1 time, under other conditions, the average ROA increases by 
0.0245583 times. 
+ β4 = 0.0055286 illustrates that when SIZE increases by 1 time, under other conditions, the average ROA increases by 
0.0055286 times. 
Eq. (2) means: 
+ β1 = -0.2575729 indicates that when the SDR increases by 1 time, in the condition that other factors remained constant, the 
average ROA decreases by 0.2575729 times. 
+ β2 = -0.2916419 indicates that when the LDR increases by 1 time, in the condition that other factors remained constant, the 
average ROA decreases by 0.2916419 times. 
+ β3 = -0.1175987 reveals that when the FATA increases 1 time, in the condition that other factors remain unchanged, ROA 
will decrease by 0.1175987 times. 
+ β4 = 0.0245643 shows that when TDTELT increases by 1 time, under other conditions, the average ROA increases by 
0.0245643 times. 
+ β5 = 0.0050401 illustrates that when SIZE increases by 1 time, under other conditions, the average ROA increases by 
0.0050401 times. 
5. Conclusion 
This study has been conducted to investigate the impact levels of determinants including capital structure on profitability ratios 
of food processing firms listed on the Vietnam Stock Exchange. Data were collected from financial statements of 35 listed food 
processing firms for the period from 2012 to 2018 on Vietnam Stock Exchange. Through analyzing the three-adjustment model 
of Return on Assets (ROA), we see that three variables of AGE, GDP growth, and Inflation did not have impact on profitability 
(proxied by ROA). DR, SDR and LDR have maintained negative impacts on profitability. In contrast, TDTELT have had 
positive impacts on the profitability. FATA and SIZE impacted Return on Assets (ROA) without statistical significance. Based 
on the findings, some suggestions are given to listed food processing firms to establish a financial structure to improve the 
profitability, so listed firms need to take the following actions such as (i) recognize the importance of financial structure that 
directly affects business performance and business risk. For capital structure, indicators such as Debt Ratio (DR), Short-term 
debt ratio (SDR) all negatively affect all variables reflecting profitability. Therefore, establishing a reasonable financial structure 
is a prerequisite to increase the profitability in the business. (ii) In addition to the financial structure, many factors affect 
profitability such as SIZE, GDP, and AGE. Thus, firms need to pay attention to the impact of these determinants on profitability, 
thereby taking measures to improvise promptly. On the side of Vietnam Food Association (VFA), the organization is of 
importance in managing and supporting the food processing firms in Vietnam. In particular, VFA is established under Decision 
No.727/KDDN-QD on November 13, 1989 by the Ministry of Industry and Trade. Vietnam Food Association is a non-profit 
and non-governmental organization. The organization was established to help individuals, organizations and businesses 
operating in food production, food processing, food trading and agricultural products in Vietnam. Vietnam Food Association 
has a great responsibility to support food processing company which are giving proposals, advice and suggestions to state 
agencies on inappropriate regimes and policies which is detrimental to construction enterprises and adversely affecting the 
general development of society. VFA bridges the gap between firms by building cooperation, association and exchange of 
experience, Science & Technology in operation for the purpose of improving business efficiency. Also, VFA becomes a focal 
H. Q. Bui et al. /Accounting 6 (2020) 449
point to recognize the difficulties of listed firms and advising the government on programs to support business development and 
increase production value. On the side of Stock Exchanges in Vietnam (Stock Exchange of Hanoi: HNX; and Stock Exchange 
of Ho Chi Minh City: HOSE), they play a great role in managing, encouraging listed food processing firms to improve. 
Therefore, the Stock Exchange should create conditions for businesses in the following aspects which are merging HOSE and 
HNX in order to facilitate listed firms to have the same operating environment. Also, it strengthens the monitoring function for 
listed firms, create strong sanctions against companies that intentionally cheat information. Based on the data of listed food 
firms, the Stock Exchange should calculate more than the average economic indicators of the industry, from which businesses 
can compare and position themselves. 
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Appendix No. 1 ROA model 1 
(command: xtreg ROA, DR, FATA, TDTELT, SIZE, fe) 
ROA Coef. Std.Err. T P>|t| 95% Conf.Interval 
DR -0.263622 0.0263851 -9.98 0.000 -0.3154605 -0.2112639 
FATA -0.0659737 0.0362825 -1.82 0.071 -0.1376148 0.0056673 
TDTELT -0.0234043 0.003601 6.5 0.000 0.016294 0.0305146 
SIZE -0.0285042 0.0147673 1.93 0.055 -0.0006543 0.0576628 
Cons 0.0523894 0.0891142 0.59 0.557 -0.1235697 0.2283486 
Sigma u 0.05004512 
Sigma e 0.03887115 
Rho 0.6237147 
F test that all u_i=0: F(27, 164) = 9.97 Prob > F = 0.0000 
(command: hausman fem rem) 
 Coefficient 
 (b) (B) (b-B) sqrt(diag(V_b-V_B)) 
 Fem rem Difference S.E. 
DR -0.2633622 -0.28411 0.0207478 
FATA -0.0659737 -0.030132 -0.0358417 0.011812 
TDTELT 0.0234043 0.0235507 -0.0001465 
SIZE 0.0285042 0.0258576 0.0026467 0.0104153 
 b = consistent under Ho and Ha; obtained from xtreg 
 B = inconsistent under Ha, efficient under Ho; obtained from xtreg 
 Test: Ho: difference in coefficients not systematic 
 Chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 35.64 
 450
 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 
(command: xttest3) 
Modified Wald test for groupwise heteroskedasticity 
in fixed effect regression model: 
H0: sigma(i)^2 = sigma^2 for all i 
chi2 (28) = 2188.27 
Prob>chi2 = 0.0000 
(command: xtserial ROA, DR, FATA, TDTELT, SIZE) 
Wooldridge test for autocorrelation in panel data 
H0: no first order autocorrelation 
 F( 1, 34) = 0.1243 
 Prob > F = 0.1587 
(command: collin ROA, DR, FATA, TDTELT, SIZE) 
Variables VIF SQRT VIF Tolerance R-squared 
ROA 1.54 1.24 0.6511 0.3489 
DR 1.95 1.40 0.5137 0.4863 
FATA 1.18 1.09 0.8447 0.1533 
TDTELT 1.50 1.23 0.6653 0.3347 
SIZE 1.22 1.10 0.8213 0.1787 
Mean VIF 1.48 
 Eigenval Index 
1 4.8212 1.0000 
2 0.6058 2.8211 
3 0.3787 3.5679 
4 0.1446 5.7741 
5 0.0438 10.4952 
6 0.0059 28.5072 
Condition Number 28.5072 
Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept) 
Det(correlation matrix) 0.4084 
Appendix No. 2: ROA model 2 
(command: xtreg ROA SDR LDR FATA TDTELT SIZE, fe) 
ROA Coef. Std.Err. t P>|t| [95%Conf.Interval] 
SDR -0.2593568 0.030217 -8.58 0.000 -0.3190241 -0.1996896 
LDR -0.2750795 0.0501909 -5.48 0.000 -0.3741877 -0.175913 
FATA -0.0628423 0.0381232 -1.65 0.101 -0.1381212 0.0124366 
TDTELT 0.0234813 0.003622 6.48 0.000 0.0163293 0.0306333 
SIZE 0.0278798 0.0149824 1.86 0.065 -0.0017048 0.0574644 
Cons 0.0539381 0.0895436 0.60 0.548 -0.1228769 0.2307532 
sigma u 0.0501442 
sigma e 0.0389808 
Rho 0.62332103 
F test that all u_i=0: F(27, 163) = 9.77 Prob > F = 0.0000 
(Command: hausman fem rem) 
 (b) (B) (b-B) sqrt(diag(V b-V B)) 
 fem rem Difference S.E. 
SDR -.2593568 .2719626 .0126057 
LDR -.2750795 -.3275023 .0524228 
FATA -.0628423 -.0154806 -.0473617 .0093367 
TDTELT -.0234813 -.0233672 .0001141 
H. Q. Bui et al. /Accounting 6 (2020) 451
SIZE .0278798 .0266708 .001209 .0108629 
b = consistent under Ho and Ha; obtained from xtreg 
B = inconsistent under Ha, efficient under Ho; obtained from xtreg 
Test: Ho: difference in coefficients not systematic 
chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 18.45 Prob>chi2 = 0.0024 
(command:xttest3) 
Modified Wald test for groupwise heteroskedasticity 
in fixed effect regression model: 
H0: sigma(i)^2 = sigma^2 for all i 
chi2 (28) = 2180.88; Prob>chi2 = 0.0000 
(command: xtserial ROA SDR LDR FATA TDTELT SIZE) 
Wooldridge test for autocorrelation in panel data 
H0: no first order autocorrelation 
 F( 1, 34) = 0.0458 Prob > F = 0.1587 
(Command: collin ROA SDR LDR FATA TDTELT SIZE) 
Collinearity Diagnostics 
 SQRT R- 
Variable VIF VIF Tolerance Squared 
ROA 1.57 1.25 0.6375 0.3625 
SDR 1.94 1.39 0.5148 0.4852 
LDR 1.75 1.32 0.5702 0.4298 
FATA 1.65 1.28 0.6057 0.3943 
TDTELT 1.51 1.23 0.6626 0.3374 
SIZE 1.25 1.12 0.7970 0.2030 
Mean VIF 1.61 
 Eigenval Index 
1 5.1170 1.0000 
2 0.7993 2.5301 
3 0.6167 2.8806 
4 0.3077 4.0783 
5 0.1090 6.8512 
6 0.0447 10.6989 
7 0.0056 30.2761 
Condition Number 30.2761 
 Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept) 
 Det(correlation matrix) 0.2476 
(Command:. xtreg ROA DR FATA TDTELT SIZE, fe robust) 
Fixed-effects (within) regression Number of obs = 196 
Group variable: firm1 Number of groups = 28 
R-sq: Obs per group: 
within = 0.5681 min = 7 
between = 0.5747 avg = 7.0 
overall = 0.5659 max = 7 
F(4,27) = 238.26 
corr(u_i, Xb) = 0.1454 Prob > F = 0.0000 
 452
 (Std. Err. adjusted for 28 clusters in firm1) 
ROA 
Coef. 
Robust 
Std. Err. 
t 
P>|t| 
[95% Conf. Interval] 
DR -.266642 .0290556 -9.18 0.000 -.3256901 -.2075939 
FATA -.1233716 .0630774 -1.96 0.059 -.2515603 .004817 
TDTELT .0245583 .0035209 6.98 0.000 .017403 .0317136 
SIZE .0055286 .0135332 0.41 0.685 -.0219741 .0330314 
_cons .21168 .1029421 2.06 0.047 .0024765 .4208836 
sigma_u .06397261 
sigma_e .05935437 
rho .53739436 (fraction of variance due to u_i) 
(Command: xtreg ROA SDR LDR FATA TDTELT SIZE, fe robust) 
Fixed-effects (within) regression Number of obs = 196 
Group variable: firm1 Number of groups = 28 
R-sq: Obs per group 
within = 0.4683 min = 7 
between = 0.4742 avg = 7.0 
overall = 0.4651 max = 7 
F(5,27) = 197.28 
corr (u_i,Xb) = 0.1497 Prob > F = 0.0000 
(Std. Err. adjusted for 28 clusters in firm1) 
ROA 
Coef. 
Robust 
Std. Err. 
t 
P>|t| 
[95% Conf. Interval] 
SDR -.2575729 .0310391 -8.30 0.000 -.320652 -.1944938 
LDR -.2916491 .046469 -6.28 0.000 -.3860783 -.1972054 
FATA -.1175987 .0612635 -1.92 0.063 -.2421011 .0069038 
TDTELT .0245643 .0038555 6.37 0.000 .0167289 .0323997 
SIZE .0050401 .0137663 0.37 0.717 -.0229365 .0330167 
_cons .2103563 .1030976 2.04 0.049 .0008367 .4198758 
sigma_u .06424692 
sigma_e .05947044 
rho .53855049 (fraction of variance due to u_i) 
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